Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein–protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.

中文翻译：

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从蛋白质相互作用组和基因共表达网络向基于多组学的复合网络前进：预测和提取生物学知识的方法。

从单组学数据预测生物相互作用网络已被广泛实施，以了解生物系统的各个方面。然而，最近，人们对整合多组学数据集以预测相互作用组的兴趣日益浓厚，与单一组学相比，这些数据集提供了具有更高描述能力的生物系统的全局视图。在这篇综述中，我们讨论了用于推断和分析两个最重要和研究最充分的相互作用组的各种计算方法：蛋白质-蛋白质相互作用网络和基因共表达网络。我们明确关注最近实施的方法和管道，以从这些相互作用组中推断和提取生物学上重要的信息，从利用单组学数据开始，然后发展到多组学数据。因此，还简要讨论了最近的例子和案例研究。总的来说，这篇综述将提供对蛋白质和基因网络建模的最新发展的正确理解，也将有助于从中提取实用知识。